Detecting Roads in Aerial Images Using Feature-Based Classifiers

1. A method for detecting roads in an aerial image of ground topology, comprising: determining low-level features for each pixel in an aerial image of ground topology; determining middle-level features for each pixel from the low-level features; placing a first window over each pixel, the first window including adjacent pixels to each pixel; determining high-level features for all of the pixels in the first window from the middle-level features of all of the pixels in the first window; and assigning a single probability to each pixel based on the high-level features, the single probability indicating a likelihood that the pixel is associated with a road.

2. The method of claim 1 , in which the low-level features are intensity values of the pixels, and gradients of the intensity values, and the mid-level features include orientations, edges and contours related to visual characteristics of the ground topology, and the high-level features include orientation, contour, color, line, and texture features.

3. The method of claim 1 , in which the visual characteristics are related to physical characteristics of the ground topology, the physical characteristics including geometrical, textural, and contextual features.

4. The method of claim 1 , further comprising: filtering the aerial image in a pre-processing step by applying an adaptive 2D Gaussian low-pass Wiener filter to each pixel, the pixel using a 5×5 pixel neighborhood to estimate a mean and a standard deviation of a local gradient of each pixel.

5. The method of claim 1 , further comprising: partitioning the aerial image into non-overlapping blocks; and determining an orientation of each pixel in each block.

6. The method of claim 1 , in which the probabilities form a probability image, and further comprising: filtering the probability image a median filter.

7. The method of claim 1 , in which the low-level features include intensity of the pixels, and further comprising: filtering the intensity values of the pixels; determining local intensity gradients of each pixel; constructing a gradient magnitude image from the local gradients; and filtering the gradient magnitudes to construct an orientation image, each pixel in the orientation image having an orientation.

8. The method of claim 7 , further comprising: constructing an edge image from the gradient magnitude image, the edge image including edge pixels; and constructing a contour image from the edge image.

9. The method of claim 8 , further comprising: constructing an orientation histogram within a second window centered at each pixel using the local gradients; and constructing contours by connecting the edge pixels.

10. The method of claim 9 , in which the orientation histogram associated with each second window has twelve bins in a range between 0 and π radians, the radians corresponding to the edge pixels.

11. The method of claim 10 , in which a first high-level feature is a maximum value of the orientation histogram, a second high-level feature is an entropy of the orientation histogram, a third high-level feature is an entropy of a weighted orientation histogram, a fourth high-level feature is a mean of the orientation histogram, a fifth high-level feature is a variance of the orientation histogram, a sixth high-level feature is a similarity measure between the orientation histogram and an idealized single-modal density function model of the road, and a seventh high-level feature is a weighted similarity measure.

12. The method of claim 1 , in which the first windows overlap.

13. The method of claim 1 , further comprising: normalizing values associated with the high level features to a common range of normalized probabilities.

14. The method of claim 13 , in which the normalization uses linear and non-linear functions.

15. The method of claim 13 , in which the assigning of the single probability to each pixel further comprises: aggregating the normalized probabilities of the high-level features for each pixel.